DeepONet [1]}: The DeepONet architecture has two branches, a branch net and a trunk net. In all cases, the trunk net has 3 hidden layers with 512 neurons each. The branch is a convolutional neural network, which takes inputs the spatial source term. It is extremely difficult to train the DeepONet with the full resolution of the PDE conditions because of the massive data storage requirements. Hence, for all experiments the PDE conditions are uniformly subsampled to a lower grid resolution given as an input to the branch net. The branch net has 4 DownSample blocks and 10 convolutional blocks, 2 at each down-sampled size. Additionally, the DeepONet is extremely sensitive to the sampling strategy adopted in the training data. The total number of learnable parameters is equal to 1.353 million. The subsampling of PDE conditions and the random sampling used in this work may have affected the testing accuracy of DeepONet.
